1. Technical Field
The invention disclosed broadly relates to data processing systems and more particularly relates to improvements in barcode recognition.
2. Background Art
With digital imaging techniques there is a general requirement to capture coded information that can be associated with the image. This coded information is used to classify the image and to ease later retrieval of the picture from storage.
Modern digital scanning machinery can scan a page of paper in less than one second. If a human operator then has to view the image representation and key enter the relevant code information, the effective capture speed takes, perhaps, 15-20 seconds. This reduces operator productivity and prevents the imaging application from being a cost justified exercise.
The requirement is to be able to scan the bit patterns in the digital image file and to create coded information for classifying the image.
Optical character recognition (OCR) is very sensitive to noise in the image and there is little redundancy in the digital image data representing textual characters to permit (with current art) much higher than a 98% accuracy in recognition--particularly with low resolution digital images such as come from a facsimile machine.
Optical mark reading (OMR) has been used for some time to allow mechanical classification of images with high reliability. This technique has the disadvantage that the whole image is the OMR data and so the coded data can only be used to classify related images in a set containing the OMR page.
Barcodes (of which there are probably 15-20 varieties of coding techniques) are now an everyday occurrence on many objects handled by the general public--particularly groceries, toiletries and stationery items. They are easy to generate and print on current art general purpose printers. They are sufficiently unobtrusive on documents that will be scanned into a digital imaging system that they can be an integral part of the page from which coded data needs to be extracted.
The problem to be solved is to locate the occurrence of a barcode contained in the data representing a digital image and to convert that barcode image into the corresponding coded data. The requirement is to get 100% accuracy in recognition or an indication of failure. There cannot be partial recognition or potential substitution of characters since the data will be used for classification. (Recognition of "HERE" instead of "THERE" is not acceptable.)
The requirement is to be able to recognize barcodes where accidental or malicious damage has occurred (for example, someone has written across the paper where the barcode appears). A significant amount of skew that may occur during the image capture process must also be tolerated.